1971
DOI: 10.1016/0005-1098(71)90059-8
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System identification—A survey

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Cited by 1,333 publications
(244 citation statements)
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“…The minimization in (7) is similar to the weighting for the input error case where H = G so that y(t) = Gu+Ge = G(u+e), that is, the error enters the system with the input [13].…”
Section: Inverse Identification Of Lti Systemsmentioning
confidence: 99%
“…The minimization in (7) is similar to the weighting for the input error case where H = G so that y(t) = Gu+Ge = G(u+e), that is, the error enters the system with the input [13].…”
Section: Inverse Identification Of Lti Systemsmentioning
confidence: 99%
“…For modelling a system, it is essential to evaluate precisely the relations between input and output data in a simple manner (Åström and Eykhoff, 1971). Given that neural network and genetic algorithm have the ability to model the complicated systems (Sanchez et al, 1997), many attempts have been made to introduce evolutionary methods (Farlow, 1984).…”
Section: Introductionmentioning
confidence: 99%
“…linear regression models is a widely studied subject in identification (see [1], [2]) originating from the classical results in statistics in terms of the Akaike Information Criterion (AIC) [3] and the Bayesian Information Criterion (BIC) [4]. More recently, statistical regularization (shrinkage) methods have been developed like the Non-Negative Garrote (NNG) or the Least Absolute Shrinkage and Selection Operator (LASSO) [5]- [7], or the Ridge Regression and the Elastic Net methods [8].…”
Section: Introductionmentioning
confidence: 99%